混淆矩陣 精準率 召回率 PR曲線

import numpy as np
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.multiclass import OneVsRestClassifier
from sklearn.multiclass import OneVsOneClassifier
from sklearn.metrics import precision_recall_curve

def LassoRegression(degree):
    return Pipeline([
        ("poly", PolynomialFeatures(degree=degree)),
        ("std_scaler", StandardScaler()),
        ("logic", LogisticRegression())
    ])

digits=datasets.load_digits()
X=digits.data
y=digits.target.copy()
y[digits.target==9]=1
y[digits.target!=9]=0

train_X,test_X,train_y,test_y = train_test_split(X,y,test_size=0.2,random_state=666)
# logis=LogisticRegression(multi_class="multinomial",solver="newton-cg")
logis=LogisticRegression()
logis.fit(train_X,train_y)
print(logis.score(test_X, test_y))

precision,recall,thresholds=precision_recall_curve(test_y,logis.decision_function(test_X))

plt.plot(thresholds,precision[:-1])
plt.plot(thresholds,recall[:-1])
plt.show()